from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2022-12-27 14:02:31.132106
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64('2020-12-27'),
'orange', 'inside top left'),
'Weihnachten 2020': (np.datetime64('2020-12-24'), np.datetime64('2020-12-27'),
'blue', 'inside top left'),
'3. Lockdown': (np.datetime64('2020-12-27'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 27, Dec, 2022
Time: 14:02:36
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -51.3115
Nobs: 883.000 HQIC: -51.6127
Log likelihood: 11683.0 FPE: 3.19120e-23
AIC: -51.7991 Det(Omega_mle): 2.88362e-23
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.296202 0.049533 5.980 0.000
L1.Burgenland 0.105946 0.033909 3.124 0.002
L1.Kärnten -0.106736 0.018217 -5.859 0.000
L1.Niederösterreich 0.213318 0.071118 3.000 0.003
L1.Oberösterreich 0.083204 0.067281 1.237 0.216
L1.Salzburg 0.250546 0.036006 6.959 0.000
L1.Steiermark 0.030397 0.047295 0.643 0.520
L1.Tirol 0.127194 0.038476 3.306 0.001
L1.Vorarlberg -0.061594 0.033100 -1.861 0.063
L1.Wien 0.065710 0.060008 1.095 0.274
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.062481 0.101728 0.614 0.539
L1.Burgenland -0.009178 0.069641 -0.132 0.895
L1.Kärnten 0.049269 0.037414 1.317 0.188
L1.Niederösterreich -0.171397 0.146056 -1.174 0.241
L1.Oberösterreich 0.359772 0.138177 2.604 0.009
L1.Salzburg 0.285834 0.073946 3.865 0.000
L1.Steiermark 0.109191 0.097130 1.124 0.261
L1.Tirol 0.319184 0.079020 4.039 0.000
L1.Vorarlberg 0.025196 0.067979 0.371 0.711
L1.Wien -0.024499 0.123241 -0.199 0.842
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.198984 0.025748 7.728 0.000
L1.Burgenland 0.090628 0.017627 5.142 0.000
L1.Kärnten -0.009071 0.009470 -0.958 0.338
L1.Niederösterreich 0.268120 0.036968 7.253 0.000
L1.Oberösterreich 0.109920 0.034974 3.143 0.002
L1.Salzburg 0.053625 0.018716 2.865 0.004
L1.Steiermark 0.015868 0.024585 0.645 0.519
L1.Tirol 0.102562 0.020001 5.128 0.000
L1.Vorarlberg 0.057174 0.017206 3.323 0.001
L1.Wien 0.113101 0.031193 3.626 0.000
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.103453 0.026375 3.922 0.000
L1.Burgenland 0.048035 0.018056 2.660 0.008
L1.Kärnten -0.016944 0.009700 -1.747 0.081
L1.Niederösterreich 0.198426 0.037868 5.240 0.000
L1.Oberösterreich 0.276267 0.035825 7.712 0.000
L1.Salzburg 0.117994 0.019172 6.155 0.000
L1.Steiermark 0.100375 0.025183 3.986 0.000
L1.Tirol 0.127003 0.020487 6.199 0.000
L1.Vorarlberg 0.070218 0.017625 3.984 0.000
L1.Wien -0.025732 0.031953 -0.805 0.421
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.130046 0.047565 2.734 0.006
L1.Burgenland -0.053770 0.032562 -1.651 0.099
L1.Kärnten -0.036924 0.017493 -2.111 0.035
L1.Niederösterreich 0.167368 0.068291 2.451 0.014
L1.Oberösterreich 0.131386 0.064607 2.034 0.042
L1.Salzburg 0.290803 0.034575 8.411 0.000
L1.Steiermark 0.034337 0.045415 0.756 0.450
L1.Tirol 0.161712 0.036947 4.377 0.000
L1.Vorarlberg 0.108348 0.031785 3.409 0.001
L1.Wien 0.068158 0.057623 1.183 0.237
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.059506 0.037754 1.576 0.115
L1.Burgenland 0.038923 0.025846 1.506 0.132
L1.Kärnten 0.050055 0.013885 3.605 0.000
L1.Niederösterreich 0.227918 0.054205 4.205 0.000
L1.Oberösterreich 0.266505 0.051281 5.197 0.000
L1.Salzburg 0.060039 0.027443 2.188 0.029
L1.Steiermark -0.006794 0.036048 -0.188 0.851
L1.Tirol 0.157329 0.029326 5.365 0.000
L1.Vorarlberg 0.069546 0.025229 2.757 0.006
L1.Wien 0.077542 0.045738 1.695 0.090
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.183196 0.045294 4.045 0.000
L1.Burgenland 0.017930 0.031008 0.578 0.563
L1.Kärnten -0.059893 0.016659 -3.595 0.000
L1.Niederösterreich -0.094249 0.065032 -1.449 0.147
L1.Oberösterreich 0.174019 0.061524 2.828 0.005
L1.Salzburg 0.061606 0.032925 1.871 0.061
L1.Steiermark 0.229617 0.043247 5.309 0.000
L1.Tirol 0.487547 0.035184 13.857 0.000
L1.Vorarlberg 0.051735 0.030268 1.709 0.087
L1.Wien -0.049937 0.054873 -0.910 0.363
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.157293 0.051301 3.066 0.002
L1.Burgenland -0.000178 0.035119 -0.005 0.996
L1.Kärnten 0.066526 0.018867 3.526 0.000
L1.Niederösterreich 0.201413 0.073655 2.735 0.006
L1.Oberösterreich -0.070806 0.069682 -1.016 0.310
L1.Salzburg 0.221376 0.037290 5.937 0.000
L1.Steiermark 0.111833 0.048982 2.283 0.022
L1.Tirol 0.085222 0.039849 2.139 0.032
L1.Vorarlberg 0.123981 0.034281 3.617 0.000
L1.Wien 0.104835 0.062149 1.687 0.092
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.358705 0.030417 11.793 0.000
L1.Burgenland 0.007724 0.020823 0.371 0.711
L1.Kärnten -0.025635 0.011187 -2.291 0.022
L1.Niederösterreich 0.229911 0.043672 5.265 0.000
L1.Oberösterreich 0.151025 0.041316 3.655 0.000
L1.Salzburg 0.052669 0.022110 2.382 0.017
L1.Steiermark -0.016120 0.029043 -0.555 0.579
L1.Tirol 0.122623 0.023627 5.190 0.000
L1.Vorarlberg 0.071680 0.020326 3.526 0.000
L1.Wien 0.048961 0.036850 1.329 0.184
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.039128 0.163863 0.183145 0.170552 0.145591 0.130203 0.067673 0.220802
Kärnten 0.039128 1.000000 0.002719 0.132899 0.027654 0.099958 0.432003 -0.048809 0.101863
Niederösterreich 0.163863 0.002719 1.000000 0.349224 0.173091 0.317716 0.132533 0.194841 0.342594
Oberösterreich 0.183145 0.132899 0.349224 1.000000 0.235841 0.344266 0.181087 0.181498 0.274437
Salzburg 0.170552 0.027654 0.173091 0.235841 1.000000 0.156001 0.139551 0.154778 0.142218
Steiermark 0.145591 0.099958 0.317716 0.344266 0.156001 1.000000 0.163440 0.150994 0.098160
Tirol 0.130203 0.432003 0.132533 0.181087 0.139551 0.163440 1.000000 0.125457 0.165379
Vorarlberg 0.067673 -0.048809 0.194841 0.181498 0.154778 0.150994 0.125457 1.000000 0.021031
Wien 0.220802 0.101863 0.342594 0.274437 0.142218 0.098160 0.165379 0.021031 1.000000